llama-stack-mirror/llama_stack/providers/registry/inference.py
Sébastien Han dbdc811d16
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chore: isolate bare minimum project dependencies (#2282)
# What does this PR do?

The goal is to promote the minimal set of dependencies the project needs
to run, this includes:

* dependencies needed to work with the CLI
* dependencies needed for the server to run with no providers

This also:
* Relocate redundant dependencies out of the core project and into the
  individual providers that actually require them.
* Include all necessary server dependencies so the project can run
  standalone, even without any providers.

<!-- Provide a short summary of what this PR does and why. Link to
relevant issues if applicable. -->

<!-- If resolving an issue, uncomment and update the line below -->
<!-- Closes #[issue-number] -->

## Test Plan

Build and run distro a server.

Signed-off-by: Sébastien Han <seb@redhat.com>
2025-06-26 10:14:27 +02:00

308 lines
13 KiB
Python

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
from llama_stack.providers.datatypes import (
AdapterSpec,
Api,
InlineProviderSpec,
ProviderSpec,
remote_provider_spec,
)
META_REFERENCE_DEPS = [
"accelerate",
"fairscale",
"torch",
"torchvision",
"transformers",
"zmq",
"lm-format-enforcer",
"sentence-transformers",
"torchao==0.8.0",
"fbgemm-gpu-genai==1.1.2",
]
def available_providers() -> list[ProviderSpec]:
return [
InlineProviderSpec(
api=Api.inference,
provider_type="inline::meta-reference",
pip_packages=META_REFERENCE_DEPS,
module="llama_stack.providers.inline.inference.meta_reference",
config_class="llama_stack.providers.inline.inference.meta_reference.MetaReferenceInferenceConfig",
),
InlineProviderSpec(
api=Api.inference,
provider_type="inline::vllm",
pip_packages=[
"vllm",
],
module="llama_stack.providers.inline.inference.vllm",
config_class="llama_stack.providers.inline.inference.vllm.VLLMConfig",
),
InlineProviderSpec(
api=Api.inference,
provider_type="inline::sentence-transformers",
pip_packages=[
"torch torchvision --index-url https://download.pytorch.org/whl/cpu",
"sentence-transformers --no-deps",
],
module="llama_stack.providers.inline.inference.sentence_transformers",
config_class="llama_stack.providers.inline.inference.sentence_transformers.config.SentenceTransformersInferenceConfig",
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="cerebras",
pip_packages=[
"cerebras_cloud_sdk",
],
module="llama_stack.providers.remote.inference.cerebras",
config_class="llama_stack.providers.remote.inference.cerebras.CerebrasImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="ollama",
pip_packages=["ollama", "aiohttp", "h11>=0.16.0"],
config_class="llama_stack.providers.remote.inference.ollama.OllamaImplConfig",
module="llama_stack.providers.remote.inference.ollama",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="vllm",
pip_packages=["openai"],
module="llama_stack.providers.remote.inference.vllm",
config_class="llama_stack.providers.remote.inference.vllm.VLLMInferenceAdapterConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="tgi",
pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.remote.inference.tgi",
config_class="llama_stack.providers.remote.inference.tgi.TGIImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="hf::serverless",
pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.remote.inference.tgi",
config_class="llama_stack.providers.remote.inference.tgi.InferenceAPIImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="hf::endpoint",
pip_packages=["huggingface_hub", "aiohttp"],
module="llama_stack.providers.remote.inference.tgi",
config_class="llama_stack.providers.remote.inference.tgi.InferenceEndpointImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="fireworks",
pip_packages=[
"fireworks-ai",
],
module="llama_stack.providers.remote.inference.fireworks",
config_class="llama_stack.providers.remote.inference.fireworks.FireworksImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.fireworks.FireworksProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="together",
pip_packages=[
"together",
],
module="llama_stack.providers.remote.inference.together",
config_class="llama_stack.providers.remote.inference.together.TogetherImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.together.TogetherProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="bedrock",
pip_packages=["boto3"],
module="llama_stack.providers.remote.inference.bedrock",
config_class="llama_stack.providers.remote.inference.bedrock.BedrockConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="databricks",
pip_packages=[
"openai",
],
module="llama_stack.providers.remote.inference.databricks",
config_class="llama_stack.providers.remote.inference.databricks.DatabricksImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="nvidia",
pip_packages=[
"openai",
],
module="llama_stack.providers.remote.inference.nvidia",
config_class="llama_stack.providers.remote.inference.nvidia.NVIDIAConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="runpod",
pip_packages=["openai"],
module="llama_stack.providers.remote.inference.runpod",
config_class="llama_stack.providers.remote.inference.runpod.RunpodImplConfig",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="openai",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.openai",
config_class="llama_stack.providers.remote.inference.openai.OpenAIConfig",
provider_data_validator="llama_stack.providers.remote.inference.openai.config.OpenAIProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="anthropic",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.anthropic",
config_class="llama_stack.providers.remote.inference.anthropic.AnthropicConfig",
provider_data_validator="llama_stack.providers.remote.inference.anthropic.config.AnthropicProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="gemini",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.gemini",
config_class="llama_stack.providers.remote.inference.gemini.GeminiConfig",
provider_data_validator="llama_stack.providers.remote.inference.gemini.config.GeminiProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="groq",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.groq",
config_class="llama_stack.providers.remote.inference.groq.GroqConfig",
provider_data_validator="llama_stack.providers.remote.inference.groq.config.GroqProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="fireworks-openai-compat",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.fireworks_openai_compat",
config_class="llama_stack.providers.remote.inference.fireworks_openai_compat.config.FireworksCompatConfig",
provider_data_validator="llama_stack.providers.remote.inference.fireworks_openai_compat.config.FireworksProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="llama-openai-compat",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.llama_openai_compat",
config_class="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaCompatConfig",
provider_data_validator="llama_stack.providers.remote.inference.llama_openai_compat.config.LlamaProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="together-openai-compat",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.together_openai_compat",
config_class="llama_stack.providers.remote.inference.together_openai_compat.config.TogetherCompatConfig",
provider_data_validator="llama_stack.providers.remote.inference.together_openai_compat.config.TogetherProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="groq-openai-compat",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.groq_openai_compat",
config_class="llama_stack.providers.remote.inference.groq_openai_compat.config.GroqCompatConfig",
provider_data_validator="llama_stack.providers.remote.inference.groq_openai_compat.config.GroqProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="sambanova-openai-compat",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.sambanova_openai_compat",
config_class="llama_stack.providers.remote.inference.sambanova_openai_compat.config.SambaNovaCompatConfig",
provider_data_validator="llama_stack.providers.remote.inference.sambanova_openai_compat.config.SambaNovaProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="cerebras-openai-compat",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.cerebras_openai_compat",
config_class="llama_stack.providers.remote.inference.cerebras_openai_compat.config.CerebrasCompatConfig",
provider_data_validator="llama_stack.providers.remote.inference.cerebras_openai_compat.config.CerebrasProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="sambanova",
pip_packages=["litellm"],
module="llama_stack.providers.remote.inference.sambanova",
config_class="llama_stack.providers.remote.inference.sambanova.SambaNovaImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.sambanova.config.SambaNovaProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="passthrough",
pip_packages=[],
module="llama_stack.providers.remote.inference.passthrough",
config_class="llama_stack.providers.remote.inference.passthrough.PassthroughImplConfig",
provider_data_validator="llama_stack.providers.remote.inference.passthrough.PassthroughProviderDataValidator",
),
),
remote_provider_spec(
api=Api.inference,
adapter=AdapterSpec(
adapter_type="watsonx",
pip_packages=["ibm_watson_machine_learning"],
module="llama_stack.providers.remote.inference.watsonx",
config_class="llama_stack.providers.remote.inference.watsonx.WatsonXConfig",
provider_data_validator="llama_stack.providers.remote.inference.watsonx.WatsonXProviderDataValidator",
),
),
]